Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps

Approximately 20% of Africa's population suffered from undernourishment, and 868 million people experienced moderate to severe food insecurity in 2022. Land-use and land-cover maps provide crucial insights for addressing food insecurity, e.g., by mapping croplands. The development of global land-cover maps has been facilitated by the increasing availability of earth observation data and advancements in geospatial machine learning. However, these global maps exhibit lower accuracy and inconsistencies in Africa, partly due to the lack of representative training data. To address this issue, we propose a data-centric framework with a teacher-student model setup, which uses diverse data sources of satellite images and label examples to produce local land-cover maps. Our method trains a high-resolution teacher model on images with a resolution of 0.331 m/pixel and a low-resolution student model on publicly available images with a resolution of 10 m/pixel. The student model also utilizes the teacher model's output as its weak label examples through knowledge distillation. We evaluated our framework using Murang'a County, Kenya, as a use case and achieved significant improvements, i.e., 0.14 in the F1 score and 0.21 in Intersection-over-Union, compared to the best global map. Our evaluation also revealed inconsistencies in existing global maps, with a maximum agreement rate of 0.30 among themselves. Insights obtained from our cross-collaborative work can provide valuable guidance to local and national policymakers in making informed decisions to improve resource utilization and food security.